R
Lead AI Engineer
R
Lead AI Engineer
r3 consultantThis job is no longer accepting applications
Job Description
Job Title: Lead AI Engineer
Location: Bangalore
Work Mode: Onsite / WFO
Experience: 8–12 years
Education: Bachelor's/Master's in Computer Science, Engineering, or related field (M.Tech/MS preferred but not mandatory)
Budget/CTC: Up to ₹40 LPA
Notice Period: 30 days preferred
Roles & Responsibilities
Lead end-to-end AI solution development — from architecture design through production deployment
Own AI system design with a strong emphasis on LLMs and Retrieval-Augmented Generation (RAG)
Drive creation of comprehensive POCs and data-driven AI strategies for client/business problems
Guide cross-functional teams (data engineers, ML engineers, product) and manage stakeholder expectations across Pharma, Retail, or CPG domains
Own technical decision-making on model selection, RAG architecture, cloud infrastructure, and MLOps practices
Mentor and upskill junior/mid-level AI engineers
Present AI strategy and solution trade-offs to both technical and business stakeholders
Mandatory Skills
Python, SQL
AI system design & end-to-end AI solution development (LLMs & RAG)
Cloud platforms — Azure / AWS / GCP
Data & AI solutioning (data pipelines feeding AI systems)
8–12 yrs total experience with demonstrable lead-level ownership of AI projects
Good to Have
Domain experience in Pharma, Retail, or CPG
End-to-end POC development experience
Leadership in AI project delivery (team lead/manager track record)
MLOps / CI-CD practices for AI pipelines
Must-Have Soft Skills
Leadership and people management
Strategic thinking and decision-making
Stakeholder management
Effective communication (technical ↔ non-technical)
Good-to-Have Soft Skills
Mentoring and coaching junior team members
Ability to convey complex domain concepts to mixed audiences
Location: Bangalore
Work Mode: Onsite / WFO
Experience: 8–12 years
Education: Bachelor's/Master's in Computer Science, Engineering, or related field (M.Tech/MS preferred but not mandatory)
Budget/CTC: Up to ₹40 LPA
Notice Period: 30 days preferred
Roles & Responsibilities
Lead end-to-end AI solution development — from architecture design through production deployment
Own AI system design with a strong emphasis on LLMs and Retrieval-Augmented Generation (RAG)
Drive creation of comprehensive POCs and data-driven AI strategies for client/business problems
Guide cross-functional teams (data engineers, ML engineers, product) and manage stakeholder expectations across Pharma, Retail, or CPG domains
Own technical decision-making on model selection, RAG architecture, cloud infrastructure, and MLOps practices
Mentor and upskill junior/mid-level AI engineers
Present AI strategy and solution trade-offs to both technical and business stakeholders
Mandatory Skills
Python, SQL
AI system design & end-to-end AI solution development (LLMs & RAG)
Cloud platforms — Azure / AWS / GCP
Data & AI solutioning (data pipelines feeding AI systems)
8–12 yrs total experience with demonstrable lead-level ownership of AI projects
Good to Have
Domain experience in Pharma, Retail, or CPG
End-to-end POC development experience
Leadership in AI project delivery (team lead/manager track record)
MLOps / CI-CD practices for AI pipelines
Must-Have Soft Skills
Leadership and people management
Strategic thinking and decision-making
Stakeholder management
Effective communication (technical ↔ non-technical)
Good-to-Have Soft Skills
Mentoring and coaching junior team members
Ability to convey complex domain concepts to mixed audiences
More Info
Key Skills
About Company
Skills:
PostgreSQL, Git, Docker, Rest Apis, Python, GenAI, LLMs, Microsoft Foundry, Azure OpenAI, Semantic Kernel, LangGraph, AI agents, Azure AI, Microsoft Agent Framework
Skills:
Docker, FastAPI, Kubernetes, Python, evaluation benchmarks, LLMOps, production observability, agent state loops, ground-truth dataset creation, EKS, distributed system debugging, API architecture
Skills:
Github, Functions, Data Modeling, Unit Testing, Pyspark, Nosql, Databases, Software Development Lifecycle, Docker, Python, AWS, Triggers, Sql, Numpy, Pandas, Gcp, Databricks, FastAPI, Azure, Kubernetes, embeddings, functional programming, SQL optimization, schema creation, Normalization, LLMs, cloud platforms, prompt engineering, Generative AI solutions, database architecture design, retrieval techniques
Skills:
Kubernetes, Node.js, Java, Python, Docker, Generative AI or LLM-based solutions, DevOps practices and tools, Prompt engineering
Skills:
Java, Kafka, Spring Boot, Datadog, Redis, Postgres, Helm, Kubernetes, AWS, LangChain, LangGraph, Temporal